from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting("sklearnex", config="config.yml")
reporting.run()

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.013	0.0	NaN	NaN	6.393	0.0	-1	1	NaN	NaN	0.065	0.000	0.193	0.193	See	See
5	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	NaN	6.647	0.0	-1	5	NaN	1.000	0.010	0.000	1.212	1.212	See	See
10	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	NaN	6.487	0.0	1	100	NaN	0.875	0.217	0.003	0.057	0.057	See	See
15	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	NaN	6.749	0.0	-1	100	NaN	NaN	0.050	0.000	0.239	0.239	See	See
20	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	NaN	6.693	0.0	1	5	NaN	1.000	0.001	0.000	15.802	16.075	See	See
25	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	NaN	6.615	0.0	1	1	NaN	0.975	0.062	0.001	0.194	0.194	See	See
30	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	NaN	0.328	0.0	-1	1	NaN	NaN	0.009	0.000	0.534	0.534	See	See
35	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	NaN	0.328	0.0	-1	5	NaN	1.000	0.001	0.000	6.928	7.127	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.921	0.009	NaN	NaN	0.000	0.017	-1	1	0.663	0.687	0.176	0.003	95.878	95.891	See	See
2	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.034	0.029	NaN	NaN	0.000	0.002	-1	1	0.663	1.000	0.009	0.000	214.515	214.571	See	See
3	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.313	0.010	NaN	NaN	0.000	0.313	-1	1	1.000	NaN	0.049	0.000	6.389	6.389	See	See
4	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.025	0.003	NaN	NaN	0.000	0.025	-1	1	1.000	0.742	0.180	0.004	0.137	0.137	See	See
6	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.837	0.026	NaN	NaN	0.000	0.017	-1	5	0.757	NaN	0.049	0.000	342.864	342.864	See	See
7	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	3.096	0.075	NaN	NaN	0.000	0.003	-1	5	0.757	0.875	0.216	0.002	14.333	14.333	See	See
8	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.316	0.010	NaN	NaN	0.000	0.316	-1	5	1.000	0.000	0.010	0.000	32.269	32.277	See	See
9	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.026	0.002	NaN	NaN	0.000	0.026	-1	5	1.000	NaN	0.049	0.000	0.525	0.525	See	See
11	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	17.420	0.030	NaN	NaN	0.000	0.017	1	100	0.882	0.000	0.010	0.000	1771.574	1771.831	See	See
12	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.072	0.008	NaN	NaN	0.000	0.002	1	100	0.882	NaN	0.049	0.000	42.471	42.471	See	See
13	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.307	0.006	NaN	NaN	0.000	0.307	1	100	1.000	0.742	0.183	0.009	1.678	1.680	See	See
14	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.023	0.003	NaN	NaN	0.000	0.023	1	100	1.000	1.000	0.010	0.000	2.367	2.367	See	See
16	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.848	0.064	NaN	NaN	0.000	0.017	-1	100	0.882	0.687	0.179	0.001	93.930	93.932	See	See
17	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	3.109	0.085	NaN	NaN	0.000	0.003	-1	100	0.882	1.000	0.010	0.000	306.110	306.172	See	See
18	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.309	0.006	NaN	NaN	0.000	0.309	-1	100	1.000	NaN	0.009	0.000	33.121	33.121	See	See
19	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.027	0.002	NaN	NaN	0.000	0.027	-1	100	1.000	0.967	0.027	0.000	1.006	1.006	See	See
21	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	17.102	0.157	NaN	NaN	0.000	0.017	1	5	0.757	NaN	0.009	0.000	1898.371	1898.371	See	See
22	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.069	0.009	NaN	NaN	0.000	0.002	1	5	0.757	0.974	0.029	0.001	72.205	72.283	See	See
23	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.308	0.005	NaN	NaN	0.000	0.308	1	5	1.000	1.000	0.001	0.000	414.529	422.870	See	See
24	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.022	0.000	NaN	NaN	0.000	0.022	1	5	1.000	NaN	0.009	0.000	2.465	2.465	See	See
26	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	17.388	0.176	NaN	NaN	0.000	0.017	1	1	0.663	1.000	0.001	0.000	21923.144	22443.276	See	See
27	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.193	0.005	NaN	NaN	0.001	0.001	1	1	0.663	NaN	0.009	0.000	129.278	129.278	See	See
28	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.310	0.004	NaN	NaN	0.000	0.310	1	1	1.000	0.975	0.062	0.001	4.976	4.976	See	See
29	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.021	0.000	NaN	NaN	0.000	0.021	1	1	1.000	1.000	0.001	0.000	24.677	25.167	See	See
31	KNeighborsClassifier_brute_force	True	predict	100000	1000	2	3.737	0.018	NaN	NaN	0.000	0.004	-1	1	0.896	0.974	0.028	0.000	133.949	133.954	See	See
32	KNeighborsClassifier_brute_force	False	predict	100000	1000	2	1.920	0.036	NaN	NaN	0.000	0.002	-1	1	0.896	1.000	0.001	0.000	2679.169	2772.366	See	See
33	KNeighborsClassifier_brute_force	True	predict	100000	1	2	0.241	0.004	NaN	NaN	0.000	0.241	-1	1	1.000	NaN	0.009	0.000	26.468	26.468	See	See
34	KNeighborsClassifier_brute_force	False	predict	100000	1	2	0.009	0.002	NaN	NaN	0.000	0.009	-1	1	1.000	0.967	0.027	0.000	0.336	0.336	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.928	0.0	NaN	NaN	0.020	0.0	-1	1	NaN	NaN	0.878	0.000	4.473	4.473	See	See
5	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	4.521	0.0	NaN	NaN	0.018	0.0	-1	5	NaN	1.000	0.000	0.000	9415.486	10356.585	See	See
10	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	4.575	0.0	NaN	NaN	0.017	0.0	1	100	NaN	0.940	0.689	0.011	6.636	6.637	See	See
15	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	4.700	0.0	NaN	NaN	0.017	0.0	-1	100	NaN	NaN	0.804	0.000	5.842	5.842	See	See
20	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	4.441	0.0	NaN	NaN	0.018	0.0	1	5	NaN	1.000	0.000	0.000	38597.599	50500.440	See	See
25	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	4.465	0.0	NaN	NaN	0.018	0.0	1	1	NaN	0.917	0.005	0.001	921.148	930.083	See	See
30	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.002	0.0	NaN	NaN	0.007	0.0	-1	1	NaN	NaN	0.004	0.000	0.597	0.597	See	See
35	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.001	0.0	NaN	NaN	0.024	0.0	-1	5	NaN	1.000	0.000	0.000	6.107	8.129	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	120.914	0.000	NaN	NaN	0.000	0.121	-1	1	0.929	0.910	0.130	0.002	929.849	929.941	See	See
2	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.536	0.005	NaN	NaN	0.000	0.001	-1	1	0.929	1.000	0.000	0.000	1756.076	1965.984	See	See
3	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.627	0.185	NaN	NaN	0.000	2.627	-1	1	1.000	NaN	0.786	0.000	3.342	3.342	See	See
4	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.001	NaN	NaN	0.000	0.003	-1	1	1.000	0.941	0.236	0.005	0.012	0.012	See	See
6	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	122.302	0.000	NaN	NaN	0.000	0.122	-1	5	0.946	NaN	0.816	0.000	149.824	149.824	See	See
7	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.994	0.010	NaN	NaN	0.000	0.001	-1	5	0.946	0.940	0.711	0.009	1.398	1.398	See	See
8	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.575	0.079	NaN	NaN	0.000	2.575	-1	5	1.000	1.000	0.001	0.000	3087.191	3373.053	See	See
9	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.004	0.001	NaN	NaN	0.000	0.004	-1	5	1.000	NaN	0.775	0.000	0.006	0.006	See	See
11	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	132.262	0.000	NaN	NaN	0.000	0.132	1	100	0.951	1.000	0.001	0.000	150897.657	162488.076	See	See
12	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	5.847	0.017	NaN	NaN	0.000	0.006	1	100	0.951	NaN	0.779	0.000	7.502	7.502	See	See
13	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.583	0.064	NaN	NaN	0.000	2.583	1	100	1.000	0.941	0.234	0.003	11.050	11.051	See	See
14	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.005	0.000	NaN	NaN	0.000	0.005	1	100	1.000	1.000	0.000	0.000	11.338	12.408	See	See
16	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	127.521	0.000	NaN	NaN	0.000	0.128	-1	100	0.951	0.910	0.130	0.001	980.276	980.340	See	See
17	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	3.415	0.032	NaN	NaN	0.000	0.003	-1	100	0.951	1.000	0.000	0.000	11230.313	12620.300	See	See
18	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.596	0.090	NaN	NaN	0.000	2.596	-1	100	1.000	NaN	0.001	0.000	2527.206	2527.206	See	See
19	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.006	0.001	NaN	NaN	0.000	0.006	-1	100	1.000	0.879	0.001	0.000	12.790	13.242	See	See
21	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	128.932	0.000	NaN	NaN	0.000	0.129	1	5	0.946	NaN	0.003	0.000	39238.305	39238.305	See	See
22	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	1.764	0.013	NaN	NaN	0.000	0.002	1	5	0.946	0.905	0.001	0.000	2250.146	2292.502	See	See
23	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.581	0.083	NaN	NaN	0.000	2.581	1	5	1.000	1.000	0.000	0.000	22142.964	29122.986	See	See
24	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.002	0.000	NaN	NaN	0.000	0.002	1	5	1.000	NaN	0.005	0.000	0.361	0.361	See	See
26	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	132.215	0.000	NaN	NaN	0.000	0.132	1	1	0.929	1.000	0.000	0.000	1070025.354	1375478.378	See	See
27	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.923	0.010	NaN	NaN	0.000	0.001	1	1	0.929	NaN	0.004	0.000	241.826	241.826	See	See
28	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.591	0.072	NaN	NaN	0.000	2.591	1	1	1.000	0.917	0.005	0.000	557.568	558.120	See	See
29	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.001	0.000	NaN	NaN	0.000	0.001	1	1	1.000	1.000	0.000	0.000	7.909	10.836	See	See
31	KNeighborsClassifier_kd_tree	True	predict	1000	1000	2	0.039	0.000	NaN	NaN	0.000	0.000	-1	1	0.891	0.905	0.001	0.000	53.711	55.441	See	See
32	KNeighborsClassifier_kd_tree	False	predict	1000	1000	2	0.023	0.001	NaN	NaN	0.001	0.000	-1	1	0.891	1.000	0.000	0.000	194.517	258.138	See	See
33	KNeighborsClassifier_kd_tree	True	predict	1000	1	2	0.005	0.000	NaN	NaN	0.000	0.005	-1	1	1.000	NaN	0.004	0.000	1.272	1.272	See	See
34	KNeighborsClassifier_kd_tree	False	predict	1000	1	2	0.002	0.000	NaN	NaN	0.000	0.002	-1	1	1.000	0.879	0.001	0.000	4.836	5.111	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	best_iteration	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	False	fit	1000000	1000000	2	0.723	0.0	30	NaN	0.022	0.0	random	NaN	30	NaN	0.437	0.0	1.656	1.656	See	See
3	KMeans_tall	False	fit	1000000	1000000	2	0.602	0.0	30	NaN	0.027	0.0	k-means++	NaN	30	NaN	0.463	0.0	1.299	1.299	See	See
6	KMeans_tall	False	fit	1000000	1000000	100	6.431	0.0	30	NaN	0.124	0.0	random	NaN	30	NaN	2.935	0.0	2.191	2.191	See	See
9	KMeans_tall	False	fit	1000000	1000000	100	6.229	0.0	30	NaN	0.128	0.0	k-means++	NaN	30	NaN	3.070	0.0	2.029	2.029	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	best_iteration	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	False	predict	1000000	1000	2	0.001	0.0	30	NaN	0.011	0.000	random	0.000	30	0.001	0.0	0.0	8.052	9.345	See	See
2	KMeans_tall	False	predict	1000000	1	2	0.001	0.0	30	NaN	0.000	0.001	random	1.000	30	1.000	0.0	0.0	11.589	14.810	See	See
4	KMeans_tall	False	predict	1000000	1000	2	0.001	0.0	30	NaN	0.011	0.000	k-means++	0.001	30	0.001	0.0	0.0	8.199	9.877	See	See
5	KMeans_tall	False	predict	1000000	1	2	0.001	0.0	30	NaN	0.000	0.001	k-means++	1.000	30	1.000	0.0	0.0	10.848	14.118	See	See
7	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	NaN	0.462	0.000	random	0.002	30	0.002	0.0	0.0	5.988	6.690	See	See
8	KMeans_tall	False	predict	1000000	1	100	0.001	0.0	30	NaN	0.001	0.001	random	1.000	30	1.000	0.0	0.0	9.597	11.369	See	See
10	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	NaN	0.471	0.000	k-means++	0.002	30	0.002	0.0	0.0	5.756	6.582	See	See
11	KMeans_tall	False	predict	1000000	1	100	0.001	0.0	30	NaN	0.001	0.001	k-means++	1.000	30	1.000	0.0	0.0	8.630	10.569	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	best_iteration	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	False	fit	10000	10000	2	0.077	0.0	20	NaN	0.002	0.0	random	NaN	20	NaN	0.049	0.0	1.586	1.586	See	See
3	KMeans_short	False	fit	10000	10000	2	0.229	0.0	20	NaN	0.001	0.0	k-means++	NaN	20	NaN	0.083	0.0	2.757	2.757	See	See
6	KMeans_short	False	fit	10000	10000	100	0.217	0.0	20	NaN	0.037	0.0	random	NaN	20	NaN	0.135	0.0	1.612	1.612	See	See
9	KMeans_short	False	fit	10000	10000	100	0.587	0.0	20	NaN	0.014	0.0	k-means++	NaN	20	NaN	0.337	0.0	1.742	1.742	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	best_iteration	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	False	predict	10000	1000	2	0.009	0.015	20	NaN	0.002	0.000	random	0.002	20	0.001	0.001	0.001	11.745	15.367	See	See
2	KMeans_short	False	predict	10000	1	2	0.021	0.041	20	NaN	0.000	0.021	random	1.000	20	1.000	0.000	0.000	145.434	182.751	See	See
4	KMeans_short	False	predict	10000	1000	2	0.012	0.019	20	NaN	0.001	0.000	k-means++	0.002	20	0.001	0.001	0.000	21.832	22.371	See	See
5	KMeans_short	False	predict	10000	1	2	0.002	0.001	20	NaN	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.000	11.710	14.751	See	See
7	KMeans_short	False	predict	10000	1000	100	0.003	0.001	20	NaN	0.249	0.000	random	0.321	20	0.311	0.001	0.000	2.648	2.758	See	See
8	KMeans_short	False	predict	10000	1	100	0.001	0.000	20	NaN	0.001	0.001	random	1.000	20	1.000	0.000	0.000	7.594	8.872	See	See
10	KMeans_short	False	predict	10000	1000	100	0.003	0.000	20	NaN	0.299	0.000	k-means++	0.381	20	0.286	0.001	0.000	2.402	2.428	See	See
11	KMeans_short	False	predict	10000	1	100	0.002	0.000	20	NaN	0.001	0.002	k-means++	1.000	20	1.000	0.000	0.000	7.952	9.168	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	False	fit	1000000	1000000	100	13.042	0.0	[20]	NaN	0.061	0.000	NaN	NaN	NaN	NaN	NaN	2.189	0.0	5.957	5.957	See	See
3	LogisticRegression	False	fit	1000	1000	10000	3.198	0.0	[26]	NaN	0.025	0.003	NaN	NaN	NaN	NaN	NaN	1.056	0.0	3.028	3.028	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	False	predict	1000000	1000	100	0.000	0.0	[20]	NaN	2.595	0.0	NaN	NaN	NaN	NaN	0.56	0.000	0.0	0.827	0.909	See	See
2	LogisticRegression	False	predict	1000000	1	100	0.000	0.0	[20]	NaN	0.010	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.0	0.430	0.512	See	See
4	LogisticRegression	False	predict	1000	100	10000	0.002	0.0	[26]	NaN	4.516	0.0	NaN	NaN	NaN	NaN	0.35	0.003	0.0	0.526	0.529	See	See
5	LogisticRegression	False	predict	1000	1	10000	0.000	0.0	[26]	NaN	0.736	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.0	0.150	0.156	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	False	fit	1000	1000	10000	0.196	0.0	NaN	NaN	0.409	0.0	NaN	NaN	NaN	0.191	0.0	1.024	1.024	See	See
3	Ridge	False	fit	1000000	1000000	100	1.550	0.0	NaN	NaN	0.516	0.0	NaN	NaN	NaN	1.340	0.0	1.156	1.156	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	best_iteration	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	False	predict	1000	1000	10000	0.013	0.0	NaN	NaN	6.361	0.0	NaN	NaN	0.083	0.021	0.0	0.612	0.612	See	See
2	Ridge	False	predict	1000	1	10000	0.000	0.0	NaN	NaN	0.991	0.0	NaN	NaN	NaN	0.000	0.0	0.663	0.776	See	See
4	Ridge	False	predict	1000000	1000	100	0.000	0.0	NaN	NaN	5.088	0.0	NaN	NaN	1.000	0.000	0.0	0.588	0.676	See	See
5	Ridge	False	predict	1000000	1	100	0.000	0.0	NaN	NaN	0.013	0.0	NaN	NaN	NaN	0.000	0.0	0.586	0.719	See	See